Forecasting Government Bond Spreads with Heuristic Models: Evidence from the Eurozone Periphery

Filipa Da Silva Fernandes, Charalampos Stasinakis, Zivile Zekaite

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)
90 Downloads (Pure)


This study investigates the predictability of European long-term government bond spreads through the application of heuristic and metaheuristic support vector regression (SVR) hybrid structures. Genetic, krill herd and sine–cosine algorithms are applied to the parameterization process of the SVR and locally weighted SVR (LSVR) methods. The inputs of the SVR models are selected from a large pool of linear and non-linear individual predictors. The statistical performance of the main models is evaluated against a random walk, an Autoregressive Moving Average, the best individual prediction model and the traditional SVR and LSVR structures. All models are applied to forecast daily and weekly government bond spreads of Greece, Ireland, Italy, Portugal and Spain over the sample period 2000–2017. The results show that the sine–cosine LSVR is outperforming its counterparts in terms of statistical accuracy, while metaheuristic approaches seem to benefit the parameterization process more than the heuristic ones.

Original languageEnglish
Pages (from-to)87–118
Number of pages32
JournalAnnals of Operations Research
Issue number1-2
Early online date15 Mar 2018
Publication statusPublished - Nov 2019

Bibliographical note

The final publication is available at Springer via


  • Government bond spreads
  • Support Vector Regression
  • Krill Herd
  • Sine Cosine Algorithm
  • Eurozone

ASJC Scopus subject areas

  • Economics, Econometrics and Finance(all)


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